转置转化与股票数据处理

# -*- coding:utf-8 -*-
import numpy as np
import pandas as pd
import pymysql
import sklearn
from sqlalchemy import create_engine
## 加上字符集参数，防止中文乱码
dbconn = pymysql.connect(
        host="127.0.0.1",
        database="test",
        user="root",
        password="111111",
        port=3306,
        charset='utf8')
conn = create_engine('mysql+mysqldb://root:111111@localhost:3306/yes?charset=utf8')
#上面这一大段等同于conn = create_engine('mysql+mysqldb://root:111111@localhost:3306/test?charset=utf8')

# sql语句
sqlcmd = "select * from zhuanzhi"
# 利用pandas 模块导入mysql数据
data = pd.DataFrame(pd.read_sql(sqlcmd, dbconn))
haoma=['a1','b1','c1']

data['date'] = data['date'].astype('int')

print data.dtypes

'''
dataset: DataFrame格式数据集
partionby：分组依据字段
orderby：排序依据字段
asc:是否为升序；1:升序；0:降序
return series格式：序号
'''

#实现row_number()
data['row_number']=data.groupby(data['cust_no'])['date'].rank(ascending=True,method='first')

print data





i = 0
all_doc = []
for element in haoma:
        a = data.loc[data['cust_no'] == haoma[i]]
        a = a.drop(['cust_no', 'date'], axis=1)
        a = np.array(a)
        a = a.flatten()
        a = a.tolist()
        i +=1
        all_doc.append(a)

all_doc = pd.DataFrame(all_doc)
all_doc.columns = ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

haoma =  pd.DataFrame(haoma)

jieguo = pd.DataFrame(pd.concat([haoma,all_doc],axis=1))#这一步是合并
print jieguo